Overview

Dataset statistics

Number of variables15
Number of observations2215
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory259.7 KiB
Average record size in memory120.1 B

Variable types

Numeric11
Categorical4

Alerts

Income is highly correlated with Spending and 6 other fieldsHigh correlation
Spending is highly correlated with Income and 6 other fieldsHigh correlation
Wines is highly correlated with Income and 6 other fieldsHigh correlation
Fruits is highly correlated with Income and 6 other fieldsHigh correlation
Meat is highly correlated with Income and 6 other fieldsHigh correlation
Fish is highly correlated with Income and 6 other fieldsHigh correlation
Sweets is highly correlated with Income and 6 other fieldsHigh correlation
Gold is highly correlated with Income and 6 other fieldsHigh correlation
Income is highly correlated with Spending and 5 other fieldsHigh correlation
Spending is highly correlated with Income and 6 other fieldsHigh correlation
Wines is highly correlated with Income and 2 other fieldsHigh correlation
Fruits is highly correlated with Income and 4 other fieldsHigh correlation
Meat is highly correlated with Income and 5 other fieldsHigh correlation
Fish is highly correlated with Income and 4 other fieldsHigh correlation
Sweets is highly correlated with Income and 4 other fieldsHigh correlation
Gold is highly correlated with SpendingHigh correlation
Income is highly correlated with Spending and 2 other fieldsHigh correlation
Spending is highly correlated with Income and 5 other fieldsHigh correlation
Wines is highly correlated with Income and 2 other fieldsHigh correlation
Fruits is highly correlated with Spending and 3 other fieldsHigh correlation
Meat is highly correlated with Income and 5 other fieldsHigh correlation
Fish is highly correlated with Spending and 3 other fieldsHigh correlation
Sweets is highly correlated with Fruits and 2 other fieldsHigh correlation
Gold is highly correlated with SpendingHigh correlation
Children is highly correlated with Has_childHigh correlation
Has_child is highly correlated with ChildrenHigh correlation
Income is highly correlated with Spending and 5 other fieldsHigh correlation
Spending is highly correlated with Income and 7 other fieldsHigh correlation
Has_child is highly correlated with Income and 5 other fieldsHigh correlation
Children is highly correlated with Income and 2 other fieldsHigh correlation
Wines is highly correlated with Income and 4 other fieldsHigh correlation
Fruits is highly correlated with Spending and 4 other fieldsHigh correlation
Meat is highly correlated with Income and 3 other fieldsHigh correlation
Fish is highly correlated with Spending and 6 other fieldsHigh correlation
Sweets is highly correlated with Income and 2 other fieldsHigh correlation
Gold is highly correlated with Spending and 2 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Fruits has 395 (17.8%) zeros Zeros
Fish has 379 (17.1%) zeros Zeros
Sweets has 413 (18.6%) zeros Zeros
Gold has 61 (2.8%) zeros Zeros

Reproduction

Analysis started2022-05-29 11:34:41.772973
Analysis finished2022-05-29 11:34:58.511845
Duration16.74 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct2215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1121.365688
Minimum0
Maximum2239
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:04:58.634593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119.7
Q1566.5
median1120
Q31677.5
95-th percentile2126.3
Maximum2239
Range2239
Interquartile range (IQR)1111

Descriptive statistics

Standard deviation642.8756364
Coefficient of variation (CV)0.5732970458
Kurtosis-1.193932254
Mean1121.365688
Median Absolute Deviation (MAD)556
Skewness0.0009928310651
Sum2483825
Variance413289.0839
MonotonicityStrictly increasing
2022-05-29T17:04:58.787891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
14961
 
< 0.1%
14901
 
< 0.1%
14911
 
< 0.1%
14921
 
< 0.1%
14931
 
< 0.1%
14941
 
< 0.1%
14951
 
< 0.1%
14971
 
< 0.1%
15051
 
< 0.1%
Other values (2205)2205
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
22391
< 0.1%
22381
< 0.1%
22371
< 0.1%
22361
< 0.1%
22351
< 0.1%
22341
< 0.1%
22321
< 0.1%
22311
< 0.1%
22301
< 0.1%
22291
< 0.1%

Age
Real number (ℝ≥0)

Distinct59
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.18329571
Minimum26
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:04:58.936481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile34
Q145
median52
Q363
95-th percentile72
Maximum129
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.98699954
Coefficient of variation (CV)0.2253903107
Kurtosis0.7340235972
Mean53.18329571
Median Absolute Deviation (MAD)9
Skewness0.3529119716
Sum117801
Variance143.6881581
MonotonicityNot monotonic
2022-05-29T17:04:59.101996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4689
 
4.0%
5186
 
3.9%
4783
 
3.7%
5078
 
3.5%
4476
 
3.4%
5275
 
3.4%
5774
 
3.3%
4972
 
3.3%
5370
 
3.2%
4869
 
3.1%
Other values (49)1443
65.1%
ValueCountFrequency (%)
262
 
0.1%
275
 
0.2%
283
 
0.1%
295
 
0.2%
3013
0.6%
3115
0.7%
3218
0.8%
3329
1.3%
3429
1.3%
3527
1.2%
ValueCountFrequency (%)
1291
 
< 0.1%
1231
 
< 0.1%
1221
 
< 0.1%
821
 
< 0.1%
811
 
< 0.1%
796
 
0.3%
787
0.3%
778
0.4%
7616
0.7%
7516
0.7%

Education
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Postgraduate
1961 
Undergraduate
254 

Length

Max length13
Median length12
Mean length12.11467269
Min length12

Characters and Unicode

Total characters26834
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPostgraduate
2nd rowPostgraduate
3rd rowPostgraduate
4th rowPostgraduate
5th rowPostgraduate

Common Values

ValueCountFrequency (%)
Postgraduate1961
88.5%
Undergraduate254
 
11.5%

Length

2022-05-29T17:04:59.219944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T17:04:59.374958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
postgraduate1961
88.5%
undergraduate254
 
11.5%

Most occurring characters

ValueCountFrequency (%)
a4430
16.5%
t4176
15.6%
r2469
9.2%
d2469
9.2%
e2469
9.2%
g2215
8.3%
u2215
8.3%
P1961
7.3%
o1961
7.3%
s1961
7.3%
Other values (2)508
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter24619
91.7%
Uppercase Letter2215
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4430
18.0%
t4176
17.0%
r2469
10.0%
d2469
10.0%
e2469
10.0%
g2215
9.0%
u2215
9.0%
o1961
8.0%
s1961
8.0%
n254
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P1961
88.5%
U254
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin26834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4430
16.5%
t4176
15.6%
r2469
9.2%
d2469
9.2%
e2469
9.2%
g2215
8.3%
u2215
8.3%
P1961
7.3%
o1961
7.3%
s1961
7.3%
Other values (2)508
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII26834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4430
16.5%
t4176
15.6%
r2469
9.2%
d2469
9.2%
e2469
9.2%
g2215
8.3%
u2215
8.3%
P1961
7.3%
o1961
7.3%
s1961
7.3%
Other values (2)508
 
1.9%

Marital_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
In couple
1429 
Alone
786 

Length

Max length9
Median length9
Mean length7.580586907
Min length5

Characters and Unicode

Total characters16791
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowAlone
3rd rowIn couple
4th rowIn couple
5th rowIn couple

Common Values

ValueCountFrequency (%)
In couple1429
64.5%
Alone786
35.5%

Length

2022-05-29T17:04:59.491571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T17:04:59.614632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
in1429
39.2%
couple1429
39.2%
alone786
21.6%

Most occurring characters

ValueCountFrequency (%)
n2215
13.2%
o2215
13.2%
l2215
13.2%
e2215
13.2%
I1429
8.5%
1429
8.5%
c1429
8.5%
u1429
8.5%
p1429
8.5%
A786
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13147
78.3%
Uppercase Letter2215
 
13.2%
Space Separator1429
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2215
16.8%
o2215
16.8%
l2215
16.8%
e2215
16.8%
c1429
10.9%
u1429
10.9%
p1429
10.9%
Uppercase Letter
ValueCountFrequency (%)
I1429
64.5%
A786
35.5%
Space Separator
ValueCountFrequency (%)
1429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15362
91.5%
Common1429
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2215
14.4%
o2215
14.4%
l2215
14.4%
e2215
14.4%
I1429
9.3%
c1429
9.3%
u1429
9.3%
p1429
9.3%
A786
 
5.1%
Common
ValueCountFrequency (%)
1429
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2215
13.2%
o2215
13.2%
l2215
13.2%
e2215
13.2%
I1429
8.5%
1429
8.5%
c1429
8.5%
u1429
8.5%
p1429
8.5%
A786
 
4.7%

Income
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1973
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51969.8614
Minimum1730
Maximum162397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:04:59.746039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985
Q135284
median51373
Q368487
95-th percentile83977
Maximum162397
Range160667
Interquartile range (IQR)33203

Descriptive statistics

Standard deviation21526.32009
Coefficient of variation (CV)0.4142077642
Kurtosis0.7135488169
Mean51969.8614
Median Absolute Deviation (MAD)16549
Skewness0.3473496759
Sum115113243
Variance463382456.8
MonotonicityNot monotonic
2022-05-29T17:04:59.854441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750012
 
0.5%
358604
 
0.2%
460983
 
0.1%
377603
 
0.1%
189293
 
0.1%
470253
 
0.1%
186903
 
0.1%
341763
 
0.1%
674453
 
0.1%
838443
 
0.1%
Other values (1963)2175
98.2%
ValueCountFrequency (%)
17301
< 0.1%
24471
< 0.1%
35021
< 0.1%
40231
< 0.1%
44281
< 0.1%
48611
< 0.1%
53051
< 0.1%
56481
< 0.1%
65601
< 0.1%
68351
< 0.1%
ValueCountFrequency (%)
1623971
< 0.1%
1608031
< 0.1%
1577331
< 0.1%
1572431
< 0.1%
1571461
< 0.1%
1569241
< 0.1%
1539241
< 0.1%
1137341
< 0.1%
1054711
< 0.1%
1026921
< 0.1%

Spending
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1047
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.3214447
Minimum5
Maximum2525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:04:59.979067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q169
median397
Q31048
95-th percentile1778.3
Maximum2525
Range2520
Interquartile range (IQR)979

Descriptive statistics

Standard deviation602.925291
Coefficient of variation (CV)0.9927614055
Kurtosis-0.3474766422
Mean607.3214447
Median Absolute Deviation (MAD)354
Skewness0.857445198
Sum1345217
Variance363518.9066
MonotonicityNot monotonic
2022-05-29T17:05:00.196630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2218
 
0.8%
4618
 
0.8%
5716
 
0.7%
5515
 
0.7%
4415
 
0.7%
3814
 
0.6%
3714
 
0.6%
4314
 
0.6%
2014
 
0.6%
4814
 
0.6%
Other values (1037)2063
93.1%
ValueCountFrequency (%)
51
 
< 0.1%
62
 
0.1%
84
 
0.2%
92
 
0.1%
105
0.2%
115
0.2%
122
 
0.1%
136
0.3%
143
 
0.1%
1510
0.5%
ValueCountFrequency (%)
25252
0.1%
25241
< 0.1%
24861
< 0.1%
24401
< 0.1%
23521
< 0.1%
23491
< 0.1%
23461
< 0.1%
23022
0.1%
22831
< 0.1%
22791
< 0.1%

Seniority
Real number (ℝ≥0)

Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.1501279
Minimum96.36666667
Maximum119.6666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:00.360232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum96.36666667
5-th percentile97.63333333
Q1102.3666667
median108.2
Q3114
95-th percentile118.6
Maximum119.6666667
Range23.3
Interquartile range (IQR)11.63333333

Descriptive statistics

Standard deviation6.749290903
Coefficient of variation (CV)0.06240668442
Kurtosis-1.20056394
Mean108.1501279
Median Absolute Deviation (MAD)5.8
Skewness-0.01666424152
Sum239552.5333
Variance45.55292769
MonotonicityNot monotonic
2022-05-29T17:05:00.494513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.612
 
0.5%
118.211
 
0.5%
113.033333311
 
0.5%
97.9666666711
 
0.5%
106.810
 
0.5%
97.6333333310
 
0.5%
99.29
 
0.4%
114.46666679
 
0.4%
99.633333339
 
0.4%
116.63333339
 
0.4%
Other values (652)2114
95.4%
ValueCountFrequency (%)
96.366666672
 
0.1%
96.43
0.1%
96.433333333
0.1%
96.466666674
0.2%
96.55
0.2%
96.533333332
 
0.1%
96.566666672
 
0.1%
96.65
0.2%
96.633333332
 
0.1%
96.666666672
 
0.1%
ValueCountFrequency (%)
119.66666671
 
< 0.1%
119.63333331
 
< 0.1%
119.64
0.2%
119.56666673
0.1%
119.53333335
0.2%
119.54
0.2%
119.46666671
 
< 0.1%
119.43333333
0.1%
119.44
0.2%
119.36666677
0.3%

Has_child
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Has child
1582 
No child
633 

Length

Max length9
Median length9
Mean length8.714221219
Min length8

Characters and Unicode

Total characters19302
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo child
2nd rowHas child
3rd rowNo child
4th rowHas child
5th rowHas child

Common Values

ValueCountFrequency (%)
Has child1582
71.4%
No child633
28.6%

Length

2022-05-29T17:05:00.608897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T17:05:00.711462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
child2215
50.0%
has1582
35.7%
no633
 
14.3%

Most occurring characters

ValueCountFrequency (%)
2215
11.5%
c2215
11.5%
h2215
11.5%
i2215
11.5%
l2215
11.5%
d2215
11.5%
H1582
8.2%
a1582
8.2%
s1582
8.2%
N633
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14872
77.0%
Space Separator2215
 
11.5%
Uppercase Letter2215
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c2215
14.9%
h2215
14.9%
i2215
14.9%
l2215
14.9%
d2215
14.9%
a1582
10.6%
s1582
10.6%
o633
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
H1582
71.4%
N633
28.6%
Space Separator
ValueCountFrequency (%)
2215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17087
88.5%
Common2215
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
c2215
13.0%
h2215
13.0%
i2215
13.0%
l2215
13.0%
d2215
13.0%
H1582
9.3%
a1582
9.3%
s1582
9.3%
N633
 
3.7%
o633
 
3.7%
Common
ValueCountFrequency (%)
2215
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2215
11.5%
c2215
11.5%
h2215
11.5%
i2215
11.5%
l2215
11.5%
d2215
11.5%
H1582
8.2%
a1582
8.2%
s1582
8.2%
N633
 
3.3%

Children
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
1 child
1116 
No child
633 
2 children
416 
3 children
 
50

Length

Max length10
Median length7
Mean length7.916930023
Min length7

Characters and Unicode

Total characters17536
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo child
2nd row2 children
3rd rowNo child
4th row1 child
5th row1 child

Common Values

ValueCountFrequency (%)
1 child1116
50.4%
No child633
28.6%
2 children416
 
18.8%
3 children50
 
2.3%

Length

2022-05-29T17:05:00.800939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-29T17:05:00.918495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
child1749
39.5%
11116
25.2%
no633
 
14.3%
children466
 
10.5%
2416
 
9.4%
350
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2215
12.6%
c2215
12.6%
h2215
12.6%
i2215
12.6%
l2215
12.6%
d2215
12.6%
11116
6.4%
N633
 
3.6%
o633
 
3.6%
r466
 
2.7%
Other values (4)1398
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13106
74.7%
Space Separator2215
 
12.6%
Decimal Number1582
 
9.0%
Uppercase Letter633
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c2215
16.9%
h2215
16.9%
i2215
16.9%
l2215
16.9%
d2215
16.9%
o633
 
4.8%
r466
 
3.6%
e466
 
3.6%
n466
 
3.6%
Decimal Number
ValueCountFrequency (%)
11116
70.5%
2416
 
26.3%
350
 
3.2%
Space Separator
ValueCountFrequency (%)
2215
100.0%
Uppercase Letter
ValueCountFrequency (%)
N633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13739
78.3%
Common3797
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c2215
16.1%
h2215
16.1%
i2215
16.1%
l2215
16.1%
d2215
16.1%
N633
 
4.6%
o633
 
4.6%
r466
 
3.4%
e466
 
3.4%
n466
 
3.4%
Common
ValueCountFrequency (%)
2215
58.3%
11116
29.4%
2416
 
11.0%
350
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2215
12.6%
c2215
12.6%
h2215
12.6%
i2215
12.6%
l2215
12.6%
d2215
12.6%
11116
6.4%
N633
 
3.6%
o633
 
3.6%
r466
 
2.7%
Other values (4)1398
8.0%

Wines
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.2252822
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:01.084624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median175
Q3505
95-th percentile1000.3
Maximum1493
Range1493
Interquartile range (IQR)481

Descriptive statistics

Standard deviation337.34538
Coefficient of variation (CV)1.105234067
Kurtosis0.5814930267
Mean305.2252822
Median Absolute Deviation (MAD)166
Skewness1.170183337
Sum676074
Variance113801.9054
MonotonicityNot monotonic
2022-05-29T17:05:01.253196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242
 
1.9%
137
 
1.7%
637
 
1.7%
537
 
1.7%
433
 
1.5%
830
 
1.4%
330
 
1.4%
927
 
1.2%
1225
 
1.1%
1424
 
1.1%
Other values (766)1893
85.5%
ValueCountFrequency (%)
013
 
0.6%
137
1.7%
242
1.9%
330
1.4%
433
1.5%
537
1.7%
637
1.7%
721
0.9%
830
1.4%
927
1.2%
ValueCountFrequency (%)
14931
< 0.1%
14922
0.1%
14861
< 0.1%
14782
0.1%
14621
< 0.1%
14591
< 0.1%
14491
< 0.1%
13961
< 0.1%
13941
< 0.1%
13791
< 0.1%

Fruits
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.36162528
Minimum0
Maximum199
Zeros395
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:01.422729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.3
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.80203575
Coefficient of variation (CV)1.509847565
Kurtosis4.05033202
Mean26.36162528
Median Absolute Deviation (MAD)8
Skewness2.100913856
Sum58391
Variance1584.20205
MonotonicityNot monotonic
2022-05-29T17:05:01.538402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0395
 
17.8%
1158
 
7.1%
2119
 
5.4%
3114
 
5.1%
4103
 
4.7%
767
 
3.0%
562
 
2.8%
662
 
2.8%
1250
 
2.3%
848
 
2.2%
Other values (148)1037
46.8%
ValueCountFrequency (%)
0395
17.8%
1158
 
7.1%
2119
 
5.4%
3114
 
5.1%
4103
 
4.7%
562
 
2.8%
662
 
2.8%
767
 
3.0%
848
 
2.2%
935
 
1.6%
ValueCountFrequency (%)
1992
0.1%
1971
 
< 0.1%
1943
0.1%
1932
0.1%
1901
 
< 0.1%
1891
 
< 0.1%
1852
0.1%
1841
 
< 0.1%
1833
0.1%
1811
 
< 0.1%

Meat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct554
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.0632054
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:01.657103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.5
95-th percentile687.6
Maximum1725
Range1725
Interquartile range (IQR)216.5

Descriptive statistics

Standard deviation224.3115586
Coefficient of variation (CV)1.342674816
Kurtosis5.052536556
Mean167.0632054
Median Absolute Deviation (MAD)60
Skewness2.024958032
Sum370045
Variance50315.67532
MonotonicityNot monotonic
2022-05-29T17:05:01.767309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
753
 
2.4%
550
 
2.3%
1149
 
2.2%
845
 
2.0%
642
 
1.9%
1040
 
1.8%
339
 
1.8%
937
 
1.7%
1635
 
1.6%
1234
 
1.5%
Other values (544)1791
80.9%
ValueCountFrequency (%)
01
 
< 0.1%
114
 
0.6%
230
1.4%
339
1.8%
430
1.4%
550
2.3%
642
1.9%
753
2.4%
845
2.0%
937
1.7%
ValueCountFrequency (%)
17252
0.1%
16221
< 0.1%
15821
< 0.1%
9841
< 0.1%
9811
< 0.1%
9741
< 0.1%
9681
< 0.1%
9611
< 0.1%
9512
0.1%
9461
< 0.1%

Fish
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct182
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.6510158
Minimum0
Maximum259
Zeros379
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:01.959304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.76082196
Coefficient of variation (CV)1.454431462
Kurtosis3.073430873
Mean37.6510158
Median Absolute Deviation (MAD)12
Skewness1.915655483
Sum83397
Variance2998.747622
MonotonicityNot monotonic
2022-05-29T17:05:02.157282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0379
 
17.1%
2152
 
6.9%
3128
 
5.8%
4108
 
4.9%
681
 
3.7%
766
 
3.0%
857
 
2.6%
1054
 
2.4%
1348
 
2.2%
1146
 
2.1%
Other values (172)1096
49.5%
ValueCountFrequency (%)
0379
17.1%
110
 
0.5%
2152
6.9%
3128
 
5.8%
4108
 
4.9%
51
 
< 0.1%
681
 
3.7%
766
 
3.0%
857
 
2.6%
1054
 
2.4%
ValueCountFrequency (%)
2591
 
< 0.1%
2583
0.1%
2541
 
< 0.1%
2531
 
< 0.1%
2503
0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2421
 
< 0.1%
2402
0.1%
2372
0.1%

Sweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct176
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.04063205
Minimum0
Maximum262
Zeros413
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:02.354860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile125.3
Maximum262
Range262
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.07759388
Coefficient of variation (CV)1.519106277
Kurtosis4.103032656
Mean27.04063205
Median Absolute Deviation (MAD)8
Skewness2.102681943
Sum59895
Variance1687.368719
MonotonicityNot monotonic
2022-05-29T17:05:02.540065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0413
 
18.6%
1160
 
7.2%
2123
 
5.6%
3101
 
4.6%
480
 
3.6%
565
 
2.9%
663
 
2.8%
757
 
2.6%
856
 
2.5%
1245
 
2.0%
Other values (166)1052
47.5%
ValueCountFrequency (%)
0413
18.6%
1160
 
7.2%
2123
 
5.6%
3101
 
4.6%
480
 
3.6%
565
 
2.9%
663
 
2.8%
757
 
2.6%
856
 
2.5%
942
 
1.9%
ValueCountFrequency (%)
2621
 
< 0.1%
1981
 
< 0.1%
1971
 
< 0.1%
1961
 
< 0.1%
1951
 
< 0.1%
1943
0.1%
1923
0.1%
1911
 
< 0.1%
1892
0.1%
1881
 
< 0.1%

Gold
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.97968397
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2022-05-29T17:05:02.700261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median25
Q356
95-th percentile165.3
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.82266024
Coefficient of variation (CV)1.178331801
Kurtosis3.153566099
Mean43.97968397
Median Absolute Deviation (MAD)19
Skewness1.838560604
Sum97415
Variance2685.588115
MonotonicityNot monotonic
2022-05-29T17:05:02.802927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171
 
3.2%
469
 
3.1%
368
 
3.1%
563
 
2.8%
1262
 
2.8%
262
 
2.8%
061
 
2.8%
655
 
2.5%
752
 
2.3%
1049
 
2.2%
Other values (202)1603
72.4%
ValueCountFrequency (%)
061
2.8%
171
3.2%
262
2.8%
368
3.1%
469
3.1%
563
2.8%
655
2.5%
752
2.3%
840
1.8%
943
1.9%
ValueCountFrequency (%)
3211
 
< 0.1%
2911
 
< 0.1%
2621
 
< 0.1%
2491
 
< 0.1%
2481
 
< 0.1%
2471
 
< 0.1%
2461
 
< 0.1%
2451
 
< 0.1%
2422
 
0.1%
2416
0.3%

Interactions

2022-05-29T17:04:56.132034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:42.399734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.768284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.988652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.270818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.557560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.758687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.019126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.111170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.311517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.641450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.264818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:42.531086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.868817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.093228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.361449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.659134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.881105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.128115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.224341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.408290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.745215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.425583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:42.680440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.999765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.216986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.459504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.770269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.000419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.226796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.323010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.501979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.891034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.582875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:42.832633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.116557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.332604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.591347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.906133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.110796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.353638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.439476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.608897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.070898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.710172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:42.954170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.231324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.442174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.695390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.014529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.204479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.456293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.538146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.712180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.196988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.824981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.117003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.339642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.556879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.810221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.114191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.319107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.577978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.640568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.821576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.331595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.983382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.217349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.437916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.678559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.917818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.217982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.423750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.709456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.768937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.953625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.443154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:57.152485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.327905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.553516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.801211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.048722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.342665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.544696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.834046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:52.901627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.097210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.588012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:57.302461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.445146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.670499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:45.911928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.158609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.450930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.669774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:50.968083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.012251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.247514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.744272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:57.441626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.550542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.777328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.024608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.278980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.550555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.788561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:51.065647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.117468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.375522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:55.884319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:57.572412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:43.654195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:44.877947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:46.138237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:47.407410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:48.651220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:49.910769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:51.163855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:53.212081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:54.514298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-29T17:04:56.021178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-29T17:05:02.899604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-29T17:05:03.051843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-29T17:05:03.256609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-29T17:05:03.448580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-29T17:05:03.607154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-29T17:04:57.841971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-29T17:04:58.374451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAgeEducationMarital_StatusIncomeSpendingSeniorityHas_childChildrenWinesFruitsMeatFishSweetsGold
0065PostgraduateAlone58138.01617118.466667No childNo child635885461728888
1168PostgraduateAlone46344.027100.133333Has child2 children1116216
2257PostgraduateIn couple71613.0776106.766667No childNo child426491271112142
3338PostgraduateIn couple26646.053101.000000Has child1 child114201035
4441PostgraduateIn couple58293.0422101.733333Has child1 child17343118462715
5555PostgraduateIn couple62513.0716106.133333Has child1 child520429804214
6651PostgraduateAlone55635.0590116.133333Has child1 child23565164504927
7737PostgraduateIn couple33454.0169110.266667Has child1 child7610563123
8848PostgraduateIn couple30351.046109.300000Has child1 child14024332
9972PostgraduateIn couple5648.04999.966667Has child2 children28061113

Last rows

df_indexAgeEducationMarital_StatusIncomeSpendingSeniorityHas_childChildrenWinesFruitsMeatFishSweetsGold
2205222950PostgraduateIn couple24434.05097.766667Has child2 children32820017
2206223038PostgraduateAlone11012.084112.033333Has child1 child243267123
2207223152PostgraduateAlone44802.01049118.933333No childNo child85310143131020
2208223236PostgraduateAlone26816.022119.066667No childNo child516343
2209223448PostgraduateIn couple34421.030108.466667Has child1 child337629
2210223555PostgraduateIn couple61223.01341109.066667Has child1 child7094318242118247
2211223676PostgraduateIn couple64014.044497.000000Has child3 children406030008
2212223741PostgraduateAlone56981.01241101.533333No childNo child90848217321224
2213223866PostgraduateIn couple69245.0843101.566667Has child1 child42830214803061
2214223968PostgraduateIn couple52869.0172117.100000Has child2 children843612121